On Sat, 28 Nov 2009, Sarah Valencia wrote:
Hello,
I have a data frame with 1425 observations, 539 of which are zeros. I
am trying to fit the following ZINB:
f3<-formula(Nbr_Abs~ Zone * Year + Source)
ZINB2<-zeroinfl(f3, dist="negbin", link= "logit", data=TheData,
offset=log(trans.area), trace=TRUE)
Zone is a factor with 4 levels, Year a factor with 27 levels, and
Source a factor with 3 levels. Nbr_Abs is counts of a species that
shows a high level of aggregation. These counts are offset by the area
searched per transect.
The trace output and error message are as follows:
Zero-inflated Count Model
count model: negbin with log link
zero-inflation model: binomial with logit link
dependent variable:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
539 125 72 41 33 35 35 31 15 22 22 11 13 16 13
.... (truncated for brevity)...
285 286 287 288 289 290 291 292 293 294 <NA>
0 0 0 0 0 0 0 0 0 1 0
generating starting values...done
calling optim() for ML estimation:
Error in optim(fn = loglikfun, gr = gradfun, par = c(start$count,
start$zero, :
non-finite value supplied by optim
In addition: Warning message:
In glm.fit(Z, as.integer(Y0), weights = weights, family =
binomial(link = linkstr)) :
fitted probabilities numerically 0 or 1 occurred
I get the same optim error when I run a similar call using hurdle
instead of zeroinfl. However, both commands work fine when the
interaction terms is removed ( Nbr_Abs~ Zone + Year + Source). Is this
a case of some kind of linear relationship between my covariates? In
addition, I can run a negative binomial glm with the interaction term,
which I didn't think would be possible if that were the case.
My guess is that there is (quasi-)complete separtion when you add the
interaction term, i.e., that in one of the interaction groups there are
only zero or non-zero counts. See
xtabs(~ factor(Nbr_Abs > 0) + Zone + Year, data = TheData)
In this case the maximum likelihood estimate does not exist and the same
warnings as above will occur when you try to fit a logit model for
non-zero counts:
glm(factor(Nbr_Abs > 0) ~ Zone * Year + Source, data = TheData,
family = binomial)
hth,
Z
Any help would be much appreciated!
Thanks,
Sarah
--
Sarah Valencia, PhD student
Bren School of Environmental Science and Management
University of California Santa Barbara, CA 93106
Lab: (805) 893-5054
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